code: def test_stable_diffusion_adapter_default_case(self):
      device = "cpu"  # ensure determinism for the device-dependent torch.Generator
      components = self.get_dummy_components()
      sd_pipe = StableDiffusionXLAdapterPipeline(**components)
      sd_pipe = sd_pipe.to(device)
      sd_pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_dummy_inputs(device)
      image = sd_pipe(**inputs).images
      image_slice = image[0, -3:, -3:, -1]
  
      assert image.shape == (1, 64, 64, 3)
      expected_slice = np.array(
          [
              0.5813032,
              0.60995954,
              0.47563356,
              0.5056669,
              0.57199144,
              0.4631841,
              0.5176794,
              0.51252556,
              0.47183886,
          ]
      )
      assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
  
imports: import random
  import unittest
  import numpy
  import torch
  from parameterized import parameterized
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
  import diffusers
  from diffusers import AutoencoderKL, EulerDiscreteScheduler, MultiAdapter, StableDiffusionXLAdapterPipeline, T2IAdapter, UNet2DConditionModel
  from diffusers.utils import logging
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, torch_device
  from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
label_desc: Use image node_id 3 for calling the StableDiffusionXLMultiAdapterPipelineFastTests obj's underlying member method code with example usage: obj.test_stable_diffusion_adapter_default_case() without return types